We explore why multi-objective optimization matters more than finding a single "right" answer, how Optym balances traditional algorithms with modern AI, and why garbage in, garbage out is still the biggest challenge in optimization. Shaman also shares hard-won lessons about organizational design, including why platform teams often fail and how Optym structures product teams around business problems instead.
You'll hear about the surprising parallels between airline operations and freight, why planners with 20+ years of experience won't trust black box solutions, and how LLMs can serve as an interface layer without replacing the heavy lifting of discrete optimization models.
Key Topics
✦ Wall Street to freight tech: career transitions in logistics
✦ Augmentation vs. automation in AI implementation
✦ Multi-objective optimization across airlines, LTL, and truckload
✦ Why business-focused teams beat platform teams
✦ Building enterprise software with a customer-first mindset
✦ The role of LLMs alongside traditional optimization algorithms
✦ Lessons from 25 years of bootstrapped growth
Segments
00:00 - Intro
01:06 - What is Optym? Transportation Optimization Explained
03:26 - Shaman's Background
05:51 - Goldman Sachs Experience: Life on Wall Street
07:59 - Business Beyond Money
09:14 - Parallels Between Finance and Logistics Optimization
11:25 - Customer Focus: From Southwest Airlines to LTL
13:33 - Time to Value: Delivering on Promises Consistently
15:28 - Complexity Across Transportation Modes
16:25 - The Faster Horse Problem: Balancing User Feedback with Innovation
18:15 - Multi-Objective Optimization: No Single "Right" Answer
21:12 - Balancing Traditional Algorithms with Modern AI
23:18 - Garbage In, Garbage Out: Data Quality Matters
25:49 - LLMs as Interface Layer, Not Replacement
26:00 - Why Black Box AI Doesn't Work
27:21 - Airline Network Planning: 9 Months Ahead Scheduling
29:05 - Matching Capacity to Demand: Airlines vs Trucks
30:28 - LTL Dock Optimization: Learning from Airlines
34:18 - U-Haul Pod Optimization Case Study
36:17 - Organizational Design: Platform Teams vs Business-Focused Teams
39:10 - The 80/20 Split: Dual Hats in Product Development
40:45 - Building With Care: Why Deep Domain Knowledge Takes Years
44:34 - 25 Years Bootstrapped: Growth Without VC Funding
46:16 - Multi-Objective Thinking: AI as Augmentation, Not Replacement
48:30 - Final Thoughts: Context, Culture, and Getting AI Right
Whether you're building technology for transportation or implementing AI in operations, this conversation offers a refreshing perspective on doing more with less—without losing the human expertise that makes it all work.